Data Stream Improvement Device

ABSTRACT

A system for improving a quality of a data stream including community or social network site rating data includes a mobile device to generate a graphical user interface including questions on a feedback input screen relating to business transacted with an entity. A network interface unit receives the mobile device input data and the community or social network site rating data pertaining to the entity. A text processing unit accesses a selected plurality of community or social network site databases and the mobile device input data, and applies one or more weighting factors to the data including at least one of: a site credibility rating factor, a reviewers credibility factor, an amount of reviews, a current or dated information factor, and a specificity of data factor. Improved community or social network rating data is determined and the improved data stream is transmitted.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of co-pending U.S. patent application Ser. No. 14/143,353, entitled “Systems and Methods for Determining Insurance Decisions Based on Social Network Data,” filed Dec. 30, 2013, which in turn is a continuation application of U.S. patent application Ser. No. 13/291,609, entitled “Systems and Methods for Intelligent Underwriting Based on Community and/or Social Network Data,” filed Nov. 8, 2011, now U.S. Pat. No. 8,660,864, which in turn claims benefit of and priority to U.S. Provisional Patent Application Ser. No. 61/447,372 entitled “Systems and Methods for Intelligent Underwriting Based on Community and/or Social Network Data” and filed on Feb. 28, 2011, the contents of each of which are incorporated herein by reference in their entireties for all purposes.

SUMMARY OF THE INVENTION

According to some embodiments, systems, methods, apparatus, computer program code and means for improving a quality of a data stream including community or social network site rating data from one or more internet-based community or social network sites. In one embodiment, the invention relates to a system to generate, on a mobile device, a first graphical user interface including a feedback input screen including questions relating to business transacted with an entity, receiving input data via the feedback input screen, and transmit the received input data to a network interface unit. A plurality of selected community or social network site databases corresponding to a selected plurality of community or social network sites are accessed by a network interface unit. Community or social network rating data is accessed by a text processing unit from the accessed plurality of selected community or social network site databases, the community or social network rating data generated by users of the selected plurality of community or social network sites. In addition, the data pertaining to the entity received via the mobile device feedback input screen is accessed by the text processing unit. The text processing unit then applies two or more weighting factors to the community or social network rating data, wherein said one or more weighting factors comprise at least one of: a community or social network site credibility rating factor applicable to all rating data from one of the community or network site databases, a reviewer's credibility factor applicable to all rating data from one of the users having one or more reviews in the community or social network site databases, an amount of reviews or amount of ratings factor applicable to all rating data from one of the users having one or more reviews in the one or more community or social network site databases, a current or dated information factor, and a specificity of data factor. Weighted community or social network rating data is determined by the text processing unit based on the applying. The data stream including the improved community or social network rating data is transmitted by the text processing unit via the network interface unit to a remote computer system configured to receive the data stream including the improved community or social network rating data and automatically screen entities based on the data stream including improved community or social network rating data. An input/output controller renders a second graphical user interface for analyzing the community or social network rating data and the improved community or social network rating data.

A technical effect of some embodiments of the invention is an improved data stream of community and social network rating data, which may be transmitted to different entities for many different uses, including computerized underwriting, rating and quoting to provide improved rate and pricing specificity and flexibility for policies. With these and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of the present invention.

FIG. 2 is block diagram of a system according to some embodiments of the present invention.

FIG. 3 is block diagram of a system according to some embodiments of the present invention.

FIG. 4 illustrates a method according to some embodiments of the present invention.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of an insurance underwriting system 100 according to some embodiments of the present invention. The system 100 may, for example, facilitate underwriting as well as perform the quoting, rating and pricing of certain policies using community, social and business network based data such as user ratings, profiles, reviews and recommendations. For exemplary purposes, such sites/networks may include EBAY.COM, FACEBOOK.COM, LINKEDIN.COM, ANGIESLIST.COM, TWITTER.COM, BLOGGER.COM, MYSPACE.COM, FRIENDSTER.COM, and other similar sites. In the present invention, both individual and business/commercial user ratings and recommendations from one or more of the sites may be used to underwrite, rate, offer, price, renew or otherwise evaluate insurance for one or more entities based at least in part on the social network based data.

According to some embodiments, an “automated” insurance underwriting platform 110 may be provided for accessing and evaluating the social network based data. By way of example only, the underwriting platform 110 may be associated and/or communicate with a Personal Computer (PC), an enterprise server, a database farm, and/or a consumer device. The automated insurance underwriting processing platform 110 may, according to some embodiments, perform both personal lines and commercial underwriting, create rating schedules, and price and rate individual and business policies using those rating schedules. Pursuant to some embodiments, underwriting platform 110 accesses certain social and business network rating data from sites 120, 130, 140 and 150 via network 160 to utilize for enhanced underwriting in accordance with the present invention.

As used herein, devices including those associated with the automated insurance processing platform 110, and any other device described herein may exchange information via any communication network 160 which may be one or more of a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a proprietary network, a Public Switched Telephone Network (PSTN), a Wireless Application Protocol (WAP) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (IP) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.

Sites 120, 130, 140 and 150 include certain rating information such as a star based rating 122, a grade based rating 132, a transaction based rating 142 and a recommendation based rating 152 which are used to evaluate one or more potential insureds via underwriting platform 110. Each individual rating 122-152 may be considered individually, collectively or selectively and may be combined with one or more other data sources, such as traditional underwriting data sources to perform real-time underwriting. In one embodiment, the two or more of the various ratings/scores may be combined to produce an aggregate score which may be used as an input to one or more underwriting processes.

As shown, the automated insurance processing platform 110 may include a number of modules or components, including one or more underwriting modules 112, quoting modules 114 and issuing modules 116. As will be described further below, the underwriting modules 112 may be used in conjunction with the creation and updating of one or more rating schedules for use in pricing and rating insurance policies pursuant to embodiments of the present invention. For example, in some embodiments, the underwriting modules 112 are used to analyze both conventional underwriting data such as historical loss information in conjunction with social and business network based data for use in rating and pricing business insurance policies. Referring still to FIG. 1, the quoting and issuing modules 114 and 116 may be used in conjunction with the quoting, rating and pricing of insurance policies (e.g., in response to requests for quotes received from a mobile device, web server or agents operating agent devices, etc.). Note that the underwriting module 112, quoting module 114, and/or issuing module 116 may be associated with various types of insurance policies, including automobile and home insurance policies, for individuals and/or companies.

Although a single automated insurance processing platform 110 is shown in FIG. 1, any number of such devices may be included. Moreover, various devices described herein might be combined according to embodiments of the present invention. For example, in some embodiments, the automated insurance processing platform 110 and modules 112-116 might be co-located and/or may comprise a single apparatus. In some embodiments, some or all of the underwriting analysis may be performed using a spreadsheet based program or other analytic program utilizing one or more servers or server farms in a network based environment.

The automated insurance underwriting platform 110 and the analysis modules 112 may also access information in one or more databases 170, 180. The databases may include, for example, risk characteristic data 170 and historical loss data 180 associated with previously-issued insurance policies. As will be described further below, the risk characteristic data 170 and the historical loss data 180 may be used by the analysis module 112 in the creation and updating of rating schedules for the storage in one or more rating databases 120 for use by the processing platform 110 in quoting, pricing and issuing new insurance policies.

Referring now to FIG. 2, one embodiment of the present invention is shown for utilizing social network recommendation information for insurance underwriting. System 200 communicates via network 210 to access one or more social network recommendations 220 and 230 for use in the insurance underwriting process.

System 200 also may include a computer processor or text processing unit 250. The computer processor 250 may include one or more conventional microprocessors and may operate to execute programmed instructions to provide functionality as described herein. Among other functions, the computer processor 250 may access and retrieve social network rating/recommendation information via network interface unit 260 and input/output controller 270 via system bus 280.

The computer system 200 may further include a program memory 282 that is coupled to the computer processor 250. The program memory 282 may include a random access memory 284 and a read only memory 286. System memory 282 is further coupled via bus 280 to one or more fixed storage devices 290, such as one or more hard disk drives, flash memories, tape drives or other similar storage devices. Storage devices 290 may store one or more application programs 292, an operating system 294, and one or more databases such as an underwriting database 296 for storing social network based information and/or conventional underwriting information.

System 200 may be, according to some embodiments, accessible via a Graphical User Interface (GUI) rendered at least in part by input/output controller 270. The GUI might be used, for example, to dynamically display existing insurance underwriting information, analyze social network based data and historical or demographic data to generate underwriting data, rating tables and/or pricing for one or more insurance policies.

Referring still to FIG. 2, underwriting system 200 performs processing to process and extract relevant underwriting information from accessed social network recommendation data 220 and 230. The processing and extraction of information from the social network recommendation data 220 and 230 may take one or more of a number of different forms. For example, the computer system 200 may perform certain key word searches on the text based recommendations from one or more social networks. Certain key words denoting a satisfactory or better experience with a company or vendor would positively impact the underwriting process while conversely certain key words conveying a poor or bad experience would negatively impact the underwriting process for the potential insured. Positive data may result in an approval of insurance or a reduction in premium while negative data may result in a denial of insurance or an increase in premium.

As another example, computer system 200 may perform natural language processing on the recommendation to determine whether those recommendations contain, in substance, one or more of a number of different types of statements which are relevant for underwriting. One example of natural language processing may operate to mine certain characteristic information from the various social network recommendations to determine whether a party is engaging in certain risky behavior or providing high risk products.

It is contemplated that system 200 may process recommendations in one or more languages, such English, French, Arabic, Spanish, Chinese, German, Japanese and the like. In an exemplary embodiment, underwriting analysis by system 200 also can be employed for sophisticated text analyses, wherein text can be recognized irrespective of the text language. The relationships between the various words/phrases can be clarified by using an insurance rules engines for classifying words/phrases as a predictor of certain underwriting risk.

As shown in FIG. 3, a system 300 includes a mobile device 310 in communication with a social network server 320 via network 330. Mobile device 310 may be in further communication with an insurance company 340. The mobile device 310 is coupled to capture or otherwise receive data and information associated with social network server 320. The insurance company 340 operates systems to underwrite and process insurance policies based on data received from social network server 320 and/or mobile device 310.

The mobile device 310 may be any of a number of different types of mobile devices that allow for wireless communication and that may be carried with or by a user. For example, in some embodiments, mobile device 104 is an IPHONE® from Apple, Inc., a BLACKBERRY® from RIM, a mobile phone using the Google ANDROID® operating system, a portable or tablet computer (such as the IPAD® from Apple, Inc.), a mobile device operating the Android® operating system or other portable computing device having an ability to communicate wirelessly with a remote entity such as social network server 320 and/or insurance company 340.

Device 310 is configured to display a feedback or recommendation input screen 360 which contains one or more underwriting based questions for transmission via network 330 to social network server 320 and/or insurance company server 340 for further storage in underwriting database 350. Underwriting based questions are specifically selected to garner specialized information about the potential insured as an input to the underwriting process related to the potential insured.

In operation, a user operating a mobile device 310 generally initiates or launches a browser application for accessing one or more social network web pages or sites. Once on the respective social network site, the user may be prompted to enter information about themselves, the vendor/contractor they used, the business they transacted with so that the mobile device may communicate with the social network server 320 and/or insurance company 340. In one embodiment, the social network server 320 will aggregate such information from a plurality of users and transmit them to insurance company server 340.

FIG. 4 illustrates a method that might be performed, for example, by some or all of the elements of the system 100 described with respect to FIG. 1 or system 200 described with respect to system 2 according to some embodiments. The flow charts described herein do not imply a fixed order to the steps, and embodiments of the present invention may be practiced in any order that is practicable. Note that any of the methods described herein may be performed by hardware, software, or any combination of these approaches. For example, a computer-readable storage medium may store thereon instructions that when executed by a machine result in performance according to any of the embodiments described herein.

The process 400 may be performed to generate or update an underwriting database to allow the rating, quoting, pricing and issuance of insurance policies using features of the present invention. Pursuant to some embodiments, process 400 includes initiating the underwriting process 410. In one embodiment, electronic underwriting is initiated at an agent terminal or a direct to business owner terminal where an application for insurance by the potential insured triggers the electronic underwriting process. Process 400 continues with accessing social network ratings data 420. Social network rating data may be accessed via a predetermined agreement between the insurer and one or more social networks to provide the rating information from a social network database to the insurance company. Process 400 continues further by applying weighting factors 430 to the social network data. According to some embodiments, weighting factors 434 are generated and applied in step 430. For example, to allow the calculation of a professional liability insurance premium for a business, the following weighting factors may be generated and applied: Social Network Site Credibility Rating Factor, Credibility Reviewers Factor, Amount of Reviews/Ratings Factor, Current or dated information factor and Specificity of Data Factor. These factors allow more accurate and predictive pricing of business insurance premiums, and may be created and applied using the process described below in conjunction with FIG. 4.

Process 400 continues with the performing of text mining of rating information 440. In certain instances, the social networking rating data such as shown with respect to FIG. 1 may be supplemented with recommendation type data shown with respect to FIG. 2. Either or both types of data may be used in the underwriting process of the present invention including a combination of rating data from site XX and recommendation data from site YY or both types of data from the same site or multiple sites. Process 400 continues with the combining of traditional underwriting data with social network data 450 and outputting an underwriting decision 460. In one exemplary embodiment, social network based data may be combined with a description of the potential insured's operation and the standard industrial codes (“SIC”), which are associated with the potential insured's business. Each of the SIC records are linked to underwriting guidelines established by the insurance carrier and may be combined with the social network data to perform more enhanced underwriting. According to some embodiments, certain keywords (e.g., “dangerous” or “hazardous”) might be looked for and, when found, used to adjust underwriting parameters.

Note that the process 400 might be performed in connection with a newly initiated electronic underwriting decision. According to some embodiments, the process 400 might be performed on a periodic basis (e.g., when an existing agreement is up for renewal). Moreover, the reviews and/or comments accessed by the process 400 might, according to some embodiments, be associated with a predetermined period of time (e.g., only the previous six months). As another approach, older reviews or comments could be given less weight as compared to newer ones.

In other embodiments, the social network data may be used in conjunction with one or more predictive models to take into account a large number of underwriting parameters. The predictive model(s), in various implementation, may include one or more of neural networks, Bayesian networks (such as Hidden Markov models), expert systems, decision trees, collections of decision trees, support vector machines, or other systems known in the art for addressing problems with large numbers of variables. Preferably, the predictive model(s) are trained on prior data and outcomes known to the insurance company. The specific data and outcomes analyzed vary depending on the desired functionality of the particular predictive model. The particular data parameters selected for analysis in the training process are determined by using regression analysis and/or other statistical techniques known in the art for identifying relevant variables in multivariable systems. The parameters can be selected from any of the structured data parameters stored in the present system, whether the parameters were input into the system originally in a structured format or whether they were extracted from previously unstructured text, such as from text based social network recommendation data.

In the present invention, the selection of these weighting factors are to improve the predictive power of the electronic underwriting process, as well as to increase the perceived or actual fairness of ratings/recommendations on a site by site basis. For example, more established and highly frequented social network sites may be associated with a higher credibility factor, while newer, less established sites would be associated with a relatively lower credibility factor. By way of further example, more current ratings would be accorded a higher weighting while older, less current ratings or recommendations would be weighted lower.

According to some embodiments, information about a reviewer or commenter might be used to adjust one or more weighting factors. For example, an “anonymous” reviewer might be give less weight as compared to an existing customer of an insurance company. According to other embodiments, location information associated with a reviewer or commenter might be used to adjust one or more weighting factors. For example, a reviewer who posts a comment from a work site might be given more credibility as compared to other reviewers. As other examples, a reviewer who has a high reputation on a web site (e.g., a good eBay rating), who posts many reviews, and/or who has experienced a lot of transactions with the potential insured might be associated with a relatively high weighting factor.

Other factors that might be considered include: a value associated with a job (e.g., a comment about a contractor's performance in connection with a $100,000 job might be given more weight as compared to one associated with a $1,000 job) and whether a job was for inside or outside work. Moreover, the inclusion of image, video, and/or audio information might increase a weighting factor associated with a review or comment.

The system of the present invention may be used as a gate or trigger within an underwriting process to screen or refer insurance applicants for more enhanced underwriting. In some embodiments, applicants that are to be considered for possible referral for more underwriting are selected on a real-time basis according to certain pre-determined criteria. For example, the system may automatically flag or tag applicants based on a certain threshold of negative or adverse comments and/or ratings that the applicant has received in one or more online communities and/or social networks. By way of further example, a small business applying for liability insurance may be tagged for additional underwriting if they have received two or more negative feedback comments in an online community.

Note that any number of factors could be considered in connection with a pricing model. Such factors include years in business, number of locations, policy size/type, a business credit factor, and/or a total loss amount over the prior three years. In addition to the these pricing model attributes, note that other rating attributes may be used depending on the type insurance being sold (e.g., property or general liability), especially for non-growing industry and SIC classes.

By way of example, consider a small commercial automobile insurance policy. A risk score model might include numerous individual risk characteristics and thus already impact the final premium calculation. Attributes used to calculate a risk score and to determine tier placement might include fleet size, composition of fleet (PPT vs. TTT, vehicle weight), sic/industry class, years in business, years with an insurance company, financial condition of the business, prior accident frequency, liability (including PIP), comprehensive, collision, motor vehicle record information, violations (e.g., number, severity, timing), driver's age, family members as drivers, location information, ZIP code of each location, a number of states, billing information, manual premium, separately for liability and/or physical damage. Factors may be assigned for each attribute (or in some cases based on a combination of attributes). Separate factors may, according to some embodiments, be assigned for Liability and Physical Damage. The factors for Liability might be multiplied together to produce a raw score for Liability and the same may be done for Physical Damage. The raw scores may then be averaged using the manual premium as weights. The average raw score may be translated to a Risk Score, which will have an indicated premium adjustment (tier) associated with it.

The following risk characteristics might not be included in a risk score model and instead be considered when approving additional agent requested pricing: severity and description of prior claims (if any), prior claims are minimal value, prior significant claims, existence of problem drivers (no problem drivers or 1 or more problem drivers), loss control/driver hiring practices, motor vehicle records obtained on drivers, driver training programs, low turnover, no formal loss control procedures in place, condition, safety, and maintenance of equipment, vehicles are well maintained and/or late model, no formal maintenance program, evidence of coverage lapse, evidence of continuous coverage, evidence of a gap in coverage/uninsured period, presence of other lines, coverage requested (full or restricted), primary liability limits requested (standard or non-standard).

In the present invention, an underwriting referral may be done by the system automatically e-mailing or transmitting the tagged electronic application file to an underwriter for further review. If more than one underwriter is available to receive the referral of the file, then the computer system may automatically select the underwriter who is to receive the referral based on one or more factors such as one or more attributes of the insurance/applicant, the underwriter's qualifications and/or experience, the underwriter's current workload, etc. The underwriter's role, at this point, is to review the file, confirm that the referral is warranted, proceed with further analysis/investigation of the applicant, and then make an underwriting decision based on the additional underwriting performed which was triggered by the negative social network and/or community data.

As a result of the embodiments described herein, improved underwriting, rating and pricing for personal and business insurance policies may be achieved.

The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.

Although specific hardware and data configurations have been described herein, not that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the databases described herein may be combined or stored in external systems).

Applicants have discovered that embodiments described herein may be particularly useful in connection with business insurance products. Note, however, that other types of insurance products may also benefit from the invention. For example, embodiments of the present invention may be used in conjunction with the rating, pricing and quoting of personal lines policies, homeowners policies, and other types of business insurance policies. Each of these different types of insurance policies may benefit from the use of the territory and other rating approaches described herein.

The present invention has been described in terms of several embodiments solely for the purpose of illustration. Persons skilled in the art will recognize from this description that the invention is not limited to the embodiments described, but may be practiced with modifications and alterations limited only by the spirit and scope of the appended claims. 

What is claimed is:
 1. A computer system for improving a quality of a data stream including community or social network site rating data from one or more internet-based community or social network sites, comprising: a mobile device configured to generate a first graphical user interface including a feedback input screen displaying questions relating to an entity and business transacted with the entity, receive input data via the feedback input screen, and transmit the received input data; a network interface unit configured to receive the received input data from the mobile device and the community or social network site rating data from a plurality of community or social network site databases corresponding to a plurality of community or social network sites, the community or social network site rating data generated by users of the plurality of community or social network sites and pertaining to the entity; a text processing unit coupled to the network interface unit configured to: access, by the network interface unit, a selected plurality of internet-based community or social network site databases corresponding to a selected plurality of internet-based community or social network sites; access, from the accessed selected plurality of community or social network site databases, the community or social network rating data pertaining to the entity; access the received input data pertaining to the entity received by the network interface unit from the mobile device feedback input screen; apply one or more weighting actors to the community or social network rating data to determine weighted community or social network rating data, wherein said one or more weighting factors comprise at least one of: a community or social network site credibility rating factor applicable to all rating data from one of the community or network site databases, a reviewer's credibility factor applicable to all rating data from one of the users having one or more reviews in the community or social network site databases, an amount of reviews or amount of ratings factor applicable to all rating data from one of the users having one or more reviews in the one or more community or social network site databases, a current or dated information factor, and a specificity of data factor; determine, based, on the weighting factors, improved community or social network rating data; and transmit, by the network interface unit, a data stream including the improved co munity or social network rating data and the received input data to a third-party computer system configured to receive the data stream including the improved community or social network rating data and the received input data and automatically screen entities based on the data stream including the improved community or social network rating data and the received input data; and an input/output controller configured to render a second graphical user interface for analyzing the community or social network rating data, the improved community or social network rating data, and the received input data.
 2. The computer system of claim 1, further comprising the third-party computer system configured to receive the data stream including the improved community or social network rating data and the received input data and automatically screen entities based on the data stream including the improved community or social network rating data and the received input data.
 3. The computer system of claim 2, wherein the third-party computer system is further configured to selectively trigger, responsive to the automatic screening of the entities, transmission of the data stream including the improved community or social network rating data and the received input data to another third-party computer system.
 4. The computer system of claim 1, wherein the text processing unit is further configured to, prior to applying the weighting factors, conduct key word searches on text based community or social network rating data pertaining to the entity to identify data indicative of a satisfactory experience and data indicative of a poor experience.
 5. The computer system of claim 1, wherein the community or social network rating data includes values corresponding to at least one of: star based rating data, grade based rating data, transaction based rating data, and recommendation based rating data.
 6. The computer system of claim 1, wherein the text processing unit is further configured to determine the weighted community or social network rating data based at least on scale rating data from a first one of the community or social network site databases and recommendation data from a second one of the community car sac network site databases.
 7. The computer system of claim 1, wherein a first social or community network site database has data indicative of a first credibility factor, and a second social or community network site database, corresponding to a site newer than the first social or community network site, has data indicative of a second credibility factor lower than the first credibility factor.
 8. The computer system of claim 1, wherein the text processing unit is further configured to increase a weighting factor associated with certain of the community or social network rating data responsive to a determination that the certain of the community or social network rating data includes one or more of image data, video data, and audio data.
 9. A computer-implemented method for improving a quality of a data stream including community or social network site rating data from one or more internet-based community or social network sites, comprising: generating, on a mobile device, a first graphical user interface including a feedback input screen including questions relating to an entity and business transacted with the entity, receiving input data via the feedback input screen, and transmitting the received input data to a network interface unit; accessing, by the network interface unit, a plurality of selected community or social network site databases corresponding to a selected plurality of community or social network sites; accessing, by a text processing unit coupled to the network interface unit, the community or social network rating data from the accessed plurality of selected community or social network site databases, the community or social network rating data generated by users of the selected plurality of community or social network sites; accessing, by the text processing unit, the data pertaining to the entity received via the mobile device feedback input screen; applying, by the text processing unit, two or more weighting factors to the community or social network rating data, wherein said two or more weighting factors comprise at least two of: a community or social network site credibility rating factor applicable to all rating data from one of the community or network site databases, a reviewer's credibility factor applicable to all rating data from one of the users having one or more reviews in the community or social network site databases, an amount of reviews or amount of ratings factor applicable to all rating data from one of the users having one or more reviews in the one or more community or social network site databases, a current or dated information factor, and a specificity of data factor; determining, by the text processing unit based on the applying, weighted community or social network rating data; transmitting, by the text processing unit via the network interface unit, a data stream including the improved community or social network rating data to a third-party computer system configured to: receive the data stream including the improved community or social network rating data and automatically screen entities based on the data stream including improved community or social network rating data; and rendering, by an input/output controller, a second graphical user interface for analyzing the community or social network rating data and the improved community or social network rating data.
 10. The computer-implemented method of claim 9, further comprising, prior to applying the weighting factors, conducting key word searches on text based community or social network rating data pertaining to the entity to identify data indicative of a satisfactory experience and data indicative of a poor experience.
 11. The computer-implemented method of claim 9, wherein the community or social network rating data includes values corresponding to at least one of: star based rating data, grade based rating data, transaction based rating data, and recommendation based rating data.
 12. The computer-implemented method of claim 9, further comprising determining, by the text processing unit, the weighted community or social network rating data based at least on scale rating data from a first one of the community or social network site databases and recommendation data from a second one of the community or social network site databases.
 13. The computer-implemented method of claim 9, wherein a first social or community network site database has data indicative of a first credibility factor, and a second social or community network site database, corresponding to a site newer than the first social or community network site, has data indicative of a second credibility factor, lower than the first credibility factor.
 14. The computer-implemented method of claim 9, wherein the community or social network rating data includes first rating data corresponding to a first current or dated information factor, and second rating data, less current than the first rating data, corresponding to a lower current or dated information factor than the first rating data.
 15. The computer-implemented method of claim 9, further comprising increasing, by the text processing unit, a weighting factor associated with certain of the community or social network rating data responsive to a determination that the certain of the community or social network rating data includes one or more of image data, video data, and audio data.
 6. The computer-implemented method of claim 9, further comprising referring for enhanced review, by the text processing unit, the community or social network rating data pertaining to the entity, responsive to determining that a threshold of negative or adverse comments or ratings has been exceeded by the community or social network rating data pertaining to the entity. 